Estimation of Surface Water Runoff for a Semi-Arid Area Using RS and GIS

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Article Estimation of Surface Water Runoﬀ for a Semi-Arid Area Using RS and GIS-Based SCS-CN Method

Hussein Al-Ghobari 1, Ahmed Dewidar 2,* and Abed Alataway 2

1 Agricultural Engineering Department, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia; [email protected] 2 Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia; [email protected] * Correspondence: [email protected]; Tel.: +96-601-146-73217

Received: 15 June 2020; Accepted: 4 July 2020; Published: 6 July 2020

Abstract: The proper planning of storage structures, waterways, irrigation schemes, water harvesting, erosion control structures, and groundwater development strategies requires accurate estimation of surface runoff. However, hydrologists in Saudi Arabia face serious challenges, specifically due to the rare availability of surface runoff data. In this study, the soil conservation service-curve number (SCS-CN) method integrated with geographic information system (GIS) and remote sensing (RS) was utilized to estimate the surface runoff in Wadi-Uranah basin, in the western region of Saudi Arabia. Different thematic maps such as slope, hydrologic soil group (HSG), land use/land cover (LULC), and daily rainfall have been created in GIS environment and processed to generate the curve number (CN) and surface runoff maps. Based on the soil classification results, the study area was categorized into two HSGs (B and C). The dominant HSG was group C, representing about 98.8% of the total area. The LULC analysis showed four main land use types in the study region: urban, rocks, barren soil, and agricultural areas. Furthermore, the finding results showed that CN values for the normal conditions (CNII) ranged between 74 and 93 in agricultural and both urban and rock areas, respectively. The CNII values were further corrected using slope data to derive slope-adjusted CNII. Moreover, the rainfall-runoff results showed an increase in the daily runoff of the study region with a minimum of 15 mm to a maximum of 74 mm. Another interesting result was rainfall-runoff linear regression analysis that showed a good correlation of 0.98. Additionally, the peak runoff hydrograph flows for 10-, 50-, and 100-year return periods obtained from the SCS-based dimensionless unit hydrograph were 828, 1353, and 1603 m3/s, respectively. Therefore, this study highlights that the SCS-CN method integrated with RS and GIS deserves further attention for estimating runoff of ungauged basins for better basins management and conservation purposes.

Keywords: GIS; rainfall-runoﬀ; remote sensing; SCS-CN

1. Introduction Watershed runoff plays an important role in designing hydraulic structures, controlling soil erosion, and assessing the water yield potential of the watershed [1,2]. To overcome water scarcity challenges that most arid and semi-arid regions face such as Saudi Arabia, decision-makers in that region must come up with strategies to explore new water resources or optimize the usage of the available resources. However, the provision of accurate information about runoff is barely available due to the high installation and maintenance costs of the hydrologic gauging stations. Accordingly, reliable information on watershed runoff must be continuously developed to facilitate the creation and management of watershed programs [3].

Water 2020, 12, 1924; doi:10.3390/w12071924 www.mdpi.com/journal/water Water 2020, 12, 1924 2 of 16

In the absence of runoff measurements, estimated direct runoff has shown accurate results via soil conservation service-curve number (SCS-CN), a model developed by hydrologists at the Department of Agriculture of the United States [4–6]. The SCS-CN model combines the parameters of the watershed with the climatic factors in a single entity called the curve number (CN). A high CN indicates low infiltration and high runoff, while a low CN implies high infiltration and low runoff. The SCS-N method provides adequate results without using complex data [7–10]. For instance, Liu and Li [11] computed the runoff over a watershed in the Loess Plateau of China with the SCS-CN method, and stated that the SCS-CN is an effective and successful method for estimating runoff. Moreover, Topno et al. [12] employed the SCS-CN model for simulating the annual depth of runoff over an ungauged catchment (Vindhyachal region). They revealed that the SCS-CN method can be used efficiently to estimate the depth of runoff when there is no adequate hydrological information. The SCS-CN method is mainly influenced by significant runoff-associated watershed features such as land use/land cover (LULC), hydrologic soil group (HSG), slope, and climate conditions [13–15]. The term LULC defines how a specified area is exploited. The usage of LULC can take many forms, including water bodies, built-up land, agricultural land, barren land, and roads [16]. The expression HSG was introduced by the National Resources Conservation Service (NRSC). NRSC classified soil types having the same physical features and runoff characteristics into specified HSGs groups [17]. These HSGs namely A, B, C, D, A/D, B/D, and C/D have been rated in descending order to the infiltration rate. In other words, Group A refers to soils with high rates of infiltration (low potential runoff) while group D refers to soils that have low rates of infiltration (high potential runoff). Soil types which have moderate and low infiltration rates are classified as group B and C, respectively [17]. According to the effect of the slope on the SCS-CN model, it has been demonstrated that the slope can affect the estimated surface runoff in three ways, including initial abstraction reduction [18], infiltration reduction [19], and decrease of the recession time of overland flow [20]. In the Loess Plateau (China), Fang et al. [21] found that greater gradients of the slope can lead to larger flow velocities, which gives less time for rainfall to be infiltrated into the soil on one hand, and on the other hand, increase the runoff generation capacity. Furthermore, Chaudhary et al. [22] and Jha et al. [23] have experimentally investigated the impact of watershed slope on the generation of runoff and CN for a given soil (HSG with class C) and sugarcane. They found that the higher the slopes, the greater the runoff and vice-versa. The CN can be calculated from the readily available tables and curves, but this traditional method is very tedious besides, it consumes a large portion of hydrologic modeling time. In contrast, the use of the geographic information system (GIS) and remote sensing (RS) together with hydrologic models gives considerable reduction in both the cost and time with high reliability and accuracy over the traditional methods [24]. Globally, many studies have been conducted using GIS and RS techniques to simulate the surface runoff [25]. Nayak and Jaiswal [26] found that the measured and simulated depths of the runoff by the GIS based SCS-CN method were well correlated. Geena and Ballukraya [27] conducted a study in red hills watershed thereby developed CNs suited for Indian conditions using the SCS-CN method and GIS. In their study, they found that the GIS and RS tools can be accurately used to estimate the spatial hydrological parameters and temporal variables. Another study was carried out by Gitika and Ranjan [28] in the Buriganga watershed (India) to estimate the surface runoff using the GIS and RS technologies. The study results concluded that both the GIS and RS technologies are powerful tools for estimating runoff generation in the geo-hydrologic environment. The objective of this study was to apply the RS and GIS-based SCS-CN model for simulating the surface runoff to support the decision-making process for the future development of water resources and hydraulic structures in the area. Water 2020, 12, 1924 3 of 16

2. Materials and Methods

Water 2020, 12, x FOR PEER REVIEW 3 of 17 2.1. Description of the Study Area ThisThis study study was was conducted conducted at the at Wadi-Uranahthe Wadi-Uranah basin basin located located in the in western the western region region of Saudi of Saudi Arabia. Arabia. The basin considered in this study is geo-graphically located within the latitude of 21°01’30’’ The basin considered in this study is geo-graphically located within the latitude of 21◦010300 N to N to 21°30’30’’ N and longitude of 39°12’00’’ to 40°18’00’’ E. The basin covers a total area of about 2 21◦300300 N and longitude of 39◦120000 to 40◦180000 E. The basin covers a total area of about 2121.5 km , 2 with2121.5 an elevation km , with ranges an elevation from 53 to ranges 2510 from m above 53 to mean 2510 sea m level above (Figure mean 1 sea). Wadi-Uranah’s level (Figure 1). upstream Wadi- Uranah’s upstream consists of four sub-basins, the Wadi-Numan, Wadi-Al-Sharaea, Wadi-Ibrahim consists of four sub-basins, the Wadi-Numan, Wadi-Al-Sharaea, Wadi-Ibrahim and Wadi-Mehassar, all and Wadi-Mehassar, all of which stream into Makkah's central area. Wadi-Ibrahim and Wadi- of which stream into Makkah’s central area. Wadi-Ibrahim and Wadi-Mehassar are both located within Mehassar are both located within Makkah's urban areas, while Wadi-Numan (749 km2) and Wadi- Makkah’s urban areas, while Wadi-Numan (749 km2) and Wadi-Al-Sharaea (657 km2) have fewer Al-Sharaea (657 km2) have fewer built-up areas compared to their total areas. The weather is built-up areas compared to their total areas. The weather is characterized by monsoon climate with dry characterized by monsoon climate with dry and wet seasons [29]. The average annual rainfall in the andstudy wet seasonsarea is about [29]. The101 mm average [30]. annualFurthermore, rainfall there in the are study remarkable area is climatic about 101 conditions, mm [30]. particularly Furthermore, therethe are sandy remarkable storms and climatic torrential conditions, rains. particularly the sandy storms and torrential rains.

Figure 1. (A) Location map of Wadi-Uranah basin in Saudi Arabia, (B) a magniﬁed view of the studyFigure site. 1. (A) Location map of Wadi-Uranah basin in Saudi Arabia, (B) a magnified view of the study site. 2.2. Data and Software 2.2. Data and Software For a period of 35 years, the rainfall data of the study region was obtained through the Climate PredictionFor Systema period Reanalysis of 35 years, (CFSR). the rainfall Soil data data of was the derivedstudy region from was general obtained soil through map of Saudithe Climate Arabia, MinistryPrediction of Environment, System Reanalysis Water (CFSR). and Agriculture Soil data was (MEWA). derived A from Landsat general satellite soil map imagery of Saudi downloaded Arabia, fromMinistry the United of Environment, States Geological Water Surveyand Agriculture (USGS) was (MEWA). used to A developLandsat thesatellite land imagery use/land downloaded cover (LULC) from the United States Geological Survey (USGS) was used to develop the land use/land cover (LULC) map of the study region. Specifications of the satellite data acquired for LULC analysis are

Water 2020, 12, 1924 4 of 16 mapWater of the 2020 study, 12, x FOR region. PEER REVIEW Speciﬁcations of the satellite data acquired for LULC analysis are4given of 17 in Table1. A digital elevation model (DEM) with a resolution of 30 m was downloaded from the USGS given in Table 1. A digital elevation model (DEM) with a resolution of 30 m was downloaded from to derive the elevation, slope, stream network and extents of the basin for the study area (Figure2). the USGS to derive the elevation, slope, stream network and extents of the basin for the study area ArcGIS 10.5 and ERDAS Imagine 2015 software were used to build, manage, and generate various (Figure 2). ArcGIS 10.5 and ERDAS Imagine 2015 software were used to build, manage, and generate layersvarious and maps.layers and maps.

TableTable 1. 1.Details Details ofof acquired satellite satellite image. image.

SatelliteSatellite Sensor Sensor Path/ PathRow/Row Acquisition Acquisition D Dateate SpatialSpatial Resolution Resolution Landsat 8 ETM 169/45 3 March 2020 30 m Landsat 8 ETM 169/45 3 March 2020 30 m

FigureFigure 2. Extraction 2. Extraction of of drainage drainage network network forfor thethe study region region (Wadi (Wadi-Uranah).-Uranah). (A) ( ADigital) Digital elevation elevation modelmodel map map of the of study the study area; area (B); triangular (B) triangular irregular irregular networks networks map map of the of study the study area; area (C) ﬂow; (C) direction flow mapdirection of the study map of area; the (studyD) stream area; ( orderD) stream map order of the map study of the area. study area.

2.3.2.3. Methodology Methodology FigureFigure3 shows 3 shows the the overall overall methodology methodology employed employed by the the geographic geographic information information system system (GIS) (GIS) andand soil s conservationoil conservation service-curve service-curve number number (SCS-CN)(SCS-CN) method method in in estimating estimating the the surface surface runoff runo withinff within the studiedthe studied region. region. The The GIS-based GIS-based data data base base was was used used for for building building of LULCof LULC and and hydrological hydrological soil soil group (HSG)group shapefiles. (HSG) shapefiles. HSG and HSG LULC and shapefiles LULC shapefiles were intersected were intersected in GIS in environment GIS environment to produce to produce new and smallernew polygonsand smaller associated polygons withassociated names with of HSG names and of HSG LULC. and Based LULC. on Based the intersected on the intersected LULC-HSG LULC layer- andHSG its associated layer and attributeits associated table, attr theibute curve table, number the curve (CN) number database (CN) was database constructed. was constructed. The CN grid The map CN grid map was then generated from the DEM, LULC-HSG and CN database which all are required was then generated from the DEM, LULC-HSG and CN database which all are required as inputs in as inputs in GIS. A further correction was made to the created CN map using slope data to derive GIS. A further correction was made to the created CN map using slope data to derive slope-corrected slope-corrected CN map. Finally, the raster calculator function in the GIS software was used to CNcalculate map. Finally, the surface the raster runoff calculator depth from function the slope in-corrected the GIS softwareCN map and was rainfall used to data. calculate the surface runoff depth from the slope-corrected CN map and rainfall data.

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Figure 3. FlowchartFigure 3. Flowchart showing showing the methodology the methodology employed employed by bythe geographic the geographic information information system (GIS) system (GIS) based soil conservation service-curve number (SCS-CN) model in estimating the surface runoff. based soil conservation service-curve number (SCS-CN) model in estimating the surface runoff. 2.4. Soil Texture Map 2.4. Soil Texture Map The texture of the soil is very important for determining the HSG. It is a basic parameter to study The texturethe soil of-water the soil relation is very and important the hydraulic for characteristics determining of the soils HSG. [31,32]. It is a Soil basic textures parameter are to study the soil-watercategorized relation according and the to hydraulic different fractions characteristics of each soil (sand, of the silt, soils and [clay).31,32 Typically,]. Soil texturesclassifications are categorized are given for the main constituent particle size or a combination of the most abundant particle sizes. according toA di soilfferent textural fractions class map of of Wadi each-Uranah soil (sand,basin was silt, developed and clay). by the Typically,GIS-based soil classifications data acquired are given for the mainfrom constituent the MEWA particle(Figure 4). sizeTwo different or a combination kinds of soil texture, of the namely most sandy abundant clay loam particle and loamsizes. A soil textural classwere map found of in Wadi-Uranah the study region. basinA 98.81% was of the developed region is sandy by clay the loam GIS-based soil, which is soil characteriz data acquireded from by low infiltration rates while remaining area (1.19%) is loam soil, which has a moderate infiltration the MEWA (Figurerate (Figure4). 4). Two For both di fftheerent sandy kinds clay loam of and soil loam texture, soils, the namely saturated sandy hydraulic clay conductivity loam and of loam were found in thethe study least transmissivity region. A 98.81% layer ranges of thefrom region1.5 to 5.1 iscm/h. sandy clay loam soil, which is characterized by low infiltration rates while remaining area (1.19%) is loam soil, which has a moderate infiltration rate (Figure4). For both the sandy clay loam and loam soils, the saturated hydraulic conductivity of the least transmissivityWater 2020, 12, x FOR layer PEER rangesREVIEW from 1.5 to 5.1 cm/h. 6 of 17

Figure 4. Soil textural class map of the study area. Figure 4. Soil textural class map of the study area. 2.5. Hydrologic Soil Group Map Based2.5. Hydrologic on the mapSoil Group of soil Map textures, soil of the Wadi-Uranah basin was classiﬁed into two HSGs: B and C (FigureBased on5). the As map shown of soil in textures, Figure5 soil, the of HSGthe Wadi with-Uranah class Cbasin (HSG-C) was classified occupies into approximately two HSGs: B and C (Figure 5). As shown in Figure 5, the HSG with class C (HSG-C) occupies approximately 2096.4 km2, which is 98.81% of the basin's total area whilst 1.19% of the total coverage area is HSG-B. The dominance of HSG-C indicates that the soils in the basin have a moderately fine-to-fine structure, with a slow rate of water transmission and slow rate of infiltration when thoroughly wetted. Regarding HSG-B, it has a moderate infiltration rate when thoroughly wetted and consists mainly of moderately deep to deep, moderately well to well-drained soils, with moderately fine to moderately coarse textures. Considering the relative proportion of the HSG, it can be concluded that most of the soils in the Wadi-Uranah basin contribute a large amount of surface runoff, and a small to medium proportion of the precipitation is only infiltrated into the soil.

Figure 5. Hydrologic soil group (HSG) map of the study area.

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Figure 4. Soil textural class map of the study area. Water 2020, 12, 1924 6 of 16 2.5. Hydrologic Soil Group Map Based on the map of soil textures, soil of the Wadi-Uranah basin was classified into two HSGs: 2096.4 km2, which is 98.81% of the basin’s total area whilst 1.19% of the total coverage area is B and C (Figure 5). As shown in Figure 5, the HSG with class C (HSG-C) occupies approximately HSG-B.2096.4 The km dominance2, which is of98.81% HSG-C of the indicates basin's total that area the whilst soils in1.19% the of basin the total have coverage a moderately area is HSG fine-to-fine-B. structure,The withdominance a slow of rate HSG of-C waterindicates transmission that the soils and in the slow basin rate have of a infiltration moderately whenfine-to thoroughly-fine structure wetted., Regardingwith HSG-B, a slow rate it has of a water moderate transmission infiltration and rate slow when rate of thoroughly infiltration wetted when thoroughly and consists wetted. mainly of moderatelyRegarding deep HSG to deep,-B, it has moderately a moderate wellinfiltration to well-drained rate when thoroughly soils, with wetted moderately and consists fine mainly to moderately of coarsemoderately textures. Considering deep to deep, themoderately relative well proportion to well-drained of the soils HSG,, with it can moderately be concluded fine to thatmoder mostately of the soils incoarse the Wadi-Uranah textures. Considering basin contributethe relative proport a largeion amount of the HSG, of surface it can be runo concludedff, and that a small most to of mediumthe soils in the Wadi-Uranah basin contribute a large amount of surface runoff, and a small to medium proportion of the precipitation is only infiltrated into the soil. proportion of the precipitation is only infiltrated into the soil.

Figure 5. Hydrologic soil group (HSG) map of the study area. Figure 5. Hydrologic soil group (HSG) map of the study area. 2.6. Land Use/Land Cover LULC is one of the main thematic inputs of any hydrological analysis, since it provides the current status of land use and patterns. The LULC map of the study region was developed with the help of Landsat-8 ETM satellite imagery (30 m resolution) obtained from the USGS. The pan-sharpening algorithm in ArcGIS was used to produce pan-sharpened high-resolution multispectral images (15 m) by combining the high resolution panchromatic (15 m) and low resolution multispectral (30 m) images. The maximum likelihood classifier function in ERDAS Imagine 15 was employed to classify the area of study into different classes of LULC. For identifying what the cluster represents, spectral signatures collected from training samples were used (polygons representing distinct sampling areas of the various types of LULC to be classified).

2.7. Slope Map A slope is an inclined ground surface forming an angle with the horizontal plane. A DEM with 30 m resolution was used to produce the slope and aspect analysis of the Wadi-Uranah basin. In order to ensure the continuity of water flow to the outlet of the basin, the DEM was analyzed using ArcGIS software as a pre-processing phase to eliminate sinks and flat areas. The slope map of the Wadi-Uranah basin is shown in Figure6A. As shown in Figure6A, values of basin slope vary from 0 to 300%, with an average value of 23%. Study area slopes were categorized into five main classes: very low (<20%), low (20–40%), medium (40–60%), high (60–80%), and very high (>80%). Each class occupies a percentage of 56%, 19%, 16%, 7%, and 2% of the studied area, respectively. The aspect map that was generated by ArcGIS software for the study area is shown in Figure6B. As shown in Figure6B, the area of interest is sloping from the southeast to the west. Water 2020, 12, x FOR PEER REVIEW 7 of 17

2.6. Land Use/Land Cover LULC is one of the main thematic inputs of any hydrological analysis, since it provides the current status of land use and patterns. The LULC map of the study region was developed with the help of Landsat-8 ETM satellite imagery (30 m resolution) obtained from the USGS. The pan- sharpening algorithm in ArcGIS was used to produce pan-sharpened high-resolution multispectral images (15 m) by combining the high resolution panchromatic (15 m) and low resolution multispectral (30 m) images. The maximum likelihood classifier function in ERDAS Imagine 15 was employed to classify the area of study into different classes of LULC. For identifying what the cluster represents, spectral signatures collected from training samples were used (polygons representing distinct sampling areas of the various types of LULC to be classified).

2.7. Slope Map A slope is an inclined ground surface forming an angle with the horizontal plane. A DEM with 30 m resolution was used to produce the slope and aspect analysis of the Wadi-Uranah basin. In order to ensure the continuity of water flow to the outlet of the basin, the DEM was analyzed using ArcGIS software as a pre-processing phase to eliminate sinks and flat areas. The slope map of the Wadi- Uranah basin is shown in Figure 6A. As shown in Figure 6A, values of basin slope vary from 0 to 300%, with an average value of 23%. Study area slopes were categorized into five main classes: very low (<20%), low (20–40%), medium (40–60%), high (60–80%), and very high (>80%). Each class occupies a percentage of 56%, 19%, 16%, 7%, and 2% of the studied area, respectively. The aspect map Water 2020that, 12 was, 1924 generated by ArcGIS software for the study area is shown in Figure 6B. As shown in Figure 7 of 16 6B, the area of interest is sloping from the southeast to the west.

FigureFigure 6. ( A6.) (A Slope) Slope map map of of the thestudy study area, ( (BB) )aspect aspect map map of the of thestudy study area. area.

2.8. The2.8. Soil The Conservation Soil Conservation Service Service Runo Runoffff Estimation Estimation MethodMethod The SCSThe methodSCS method of runo of runoffff is basedis based on on the the CN,CN, which quantifies quantifies the the impact impact of soil of and soil land and cover land cover on the processes of rainfall- runoff. The SCS model of runoff [4] is given in Equation (1): on the processes of rainfall- runoff. The SCS model of runoff [4] is given in Equation (1):

 2 (P λS)  − P > λS =  P+(1 λ)S Q  − (1)  0 P λS ≤ where Q; P; λ; and S are direct runoff; total rainfall; initial abstraction coefficient; and potential maximum retention, respectively. In Equation (1), λS is equal to the initial abstraction that accounts for infiltration, canopy interception, and storage of surface depression during the early part of the storm [33]. Mishra and Singh [34] noted that λ may vary between 0 and . However, for general use, λ = 0.2 is ∞ recommended [35]. The S value for the CN derived from the characteristics of the basin can be calculated as: 25400 S = 254 (2) CN − where CN is a dimensionless parameter with a value between 0 (no runoff,S = ) and 100 (all rainfall ∞ becomes runoff,S = 0). It is determined by HSG, LULC, and antecedent soil moisture condition (AMC). AMC is pre-storm indicator of basin wetness and soil storage availability. In CN method, three levels of AMC are used: AMC-I for dry, AMC-II for normal, and AMC-III for wet conditions. The seasonal rainfall limits for these three AMCs are given in Table2. The CN II for the case of AMC-II was considered in this study [7]. However, The CNII can be converted into (CNI) and (CNIII) associated with (AMC-I) and (AMC-III), respectively, through the Equations (3) and (4) shown below [36]:

4.2 CN CN = × II (3) I 10 (0.058 CN ) − × II 23 CN CN = × II (4) III 10 + (0.13 CN ) × II where CNI is a curve number applied for antecedent dry condition, CNII is a curve number applied for normal condition, and CNIII is a curve number applied for antecedent wet condition. Water 2020, 12, 1924 8 of 16

Table 2. Classiﬁcation of the antecedent soil moisture condition and the related curve number (CN).

5-Days Antecedent Rainfall (mm) AMC Curve Number Growing Season Dormant Season

I CNI <35.6 <12.7

II CNII 35.6–53.3 12.7–27.9

III CNIII >53.3 >27.9

For a basin with sub-areas having diﬀerent land uses and soil types, a composite curve number (CNc) is determined by weighting of CN values for diﬀerent sub-areas. Based on McCuen [37], the CNc can be computed using the Equation (5) shown below [36]:

n X CN A CN = i × i (5) c A i=1 where CNc is the composite CN; CNi is the CN value of the sub-region; Ai is the area of the sub-region; and A is the total basin area.

2.9. Slope-Adjusted CNII From the hydrology study point of view, it is essential to incorporate the slope values in CN and adjust the CN values accordingly. For the calculation of slope-adjusted CN values, the Huang et al. [13] approach was used to make the improvement and to incorporate the slope factor into the analysis (Equation (6)). CNIII CNII 13.86α CN α = − 1 2e− +CN (6) II 3 − II where CNIIα is slope-adjusted CN for normal conditions; CNII and CNIII are tabulated curve number dependent on basin characteristics for normal and wet conditions, respectively; and α is the average slope of the basin (m/m).

2.10. The Soil Conservation Service Unit Hydrograph A basin unit hydrograph is deﬁned as a direct runoﬀ hydrograph that results from a unit volume of excess rainfall of constant intensity, which is distributed uniformly over the drainage area [38]. The SCS method for generating a synthetic unit hydrograph is relied on a dimensionless unit hydrograph [39]. This dimensionless unit hydrograph was designed from a large number of natural unit hydrographs that developed from gauged basins with a wide range of sizes and geographical locations [40]. Both the peak time and peak discharge of the unit hydrograph are computed using the Equations (7) to (9), respectively [41]: T T = r + T (7) p 2 LAG

0.8 S 0.7 0.00136(L) 25.4 + 1 TLAG = (8) √BS 0.208 A Q qp = × × (9) Tp where Tp is the time from the start of the rainfall excess up to the peak discharge (h); Tr is the duration of rainfall excess (h); TLAG is the lag time between the center of rainfall excess and the peak discharge (h); L is the maximum distance of ﬂow (m); BS is the average basin slope (%); S is the potential maximum 3 retention calculated from the CN (mm); qp is the peak discharge (m /s); Q is direct runoﬀ (mm); and A is the basin area (km2). Water 2020, 12, 1924 9 of 16

2.11. Rainfall Data Time series rainfall records (1979–2014) acquired from the CFSR were analyzed to investigate the behavior of the maximum daily rainfall data with diﬀerent N-year return periods (10, 50, and 100 years). The temporal analysis of the rainfall data was based on the probability distribution of Log Pearson Type III, which is highly recommended for the hydrological analysis [42]. For interpolating the rainfall map of the study region, the inverse square distance weighting method (Equations (10) to (12)) was used. In this method, the interpolated value is determined by the observations of weighted values, which are computedWater in 20 such20, 12,a x FOR way PEER that REVIEW points close to each other get large weights and points further10 of 17 away get small weights [43]. The weights are inversely proportional to the distance between the interpolation point andweighted the observation values, which point are computed considered in such [43 ].a way that points close to each other get large weights and points further away get small weights [43]. The weights are inversely proportional to the distance between the interpolation point and the observation Xm point considered [43]. P x , y = w P (10) 0 0 m i × i P x , y  i=1 w P (10) 0 0 i 1 i i 1/x2 = i wi m 2 (11) P1 / xi 2 w  1/x i m 2i (11) i=11 / x  i i 1 2 x2 = (x x )2 + y y (12) i i − 0 i − 0 2 2 2 where P (x0, y0) is the estimated rainfallx at coordinates x  x  y (x 0 y, y0); Pi is the rainfall at the given(12) station i; i i0   i 0  wi is the station weight; and xi = (xi, yi) is the coordinates of the station.

where P (x0, y0) is the estimated rainfall at coordinates (x0, y0); Pi is the rainfall at the given station i; 3. Resultswi andis the Discussion station weight; and xi = (xi, yi) is the coordinates of the station.

3.1. Rainfall3. Results Map and Discussion

Rainfall3.1. Rainfall data needMap to be spatially distributed for hydrological simulation input. For this reason, the spatial distributionRainfall data need map to of be the spatially maximum distributed daily for hydrological precipitation simulation over the input. Wadi-Uranah For this reason basin, was constructedthe spatial (Figure distribution7). It is obvious map of the from maximum Figure daily7 that precipitation the maximum over the daily Wadi rainfall-Uranah variesbasin was from 47 to 65 mm. Theconstructed reason ( forFigure this 7). variation It is obvious is from that theFigure area 7 that of concernthe maximum is located daily rainfall in the varies Arabian from Shield,47 to which, on the one65 mm. hand, The is reason an elevated for this variatio arean and, is that on the the area other of concern hand, is located forms in a the barrier Arabian parallel Shield, which, to the Red Sea. on the one hand, is an elevated area and, on the other hand, forms a barrier parallel to the Red Sea. In turn, thisIn turn, creates this creates diversity diversity in the in the geographic geographic distributiondistribution of ofthe the rainfall rainfall in the in area the being area studied. being studied.

Figure 7. Maximum daily precipitation distribution map of the study area.

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3.2. Land Use/Land Cover Map 3.2. Land Use/Land Cover Map Using the techniques described in Section 2.6, four major LULC classes were found, i.e., bare Using the techniques described in Section 2.6, four major LULC classes were found, i.e., bare rocks, bare soils, built-up areas and agricultural land (Figure 8). The bare rocks which cover 69% of rocks, bare soils, built-up areas and agricultural land (Figure8). The bare rocks which cover 69% of the total area were the most predominant LULC. Bare soils and built-up areas were found to cover the total area were the most predominant LULC. Bare soils and built-up areas were found to cover approximately 25% and 4% of the total area, respectively. As for the vegetation, it is clear that the approximately 25% and 4% of the total area, respectively. As for the vegetation, it is clear that the basin basin has a low vegetation cover (2%). Overall, a strong relationship between rainfall and runoff has a low vegetation cover (2%). Overall, a strong relationship between rainfall and runoff could be could be observed as a result of low vegetation coverage. Similar results were obtained by Zhao et al. observed as a result of low vegetation coverage. Similar results were obtained by Zhao et al. [44] which [44] which indicated that there is a strong relationship between runoff volume and vegetation cover, indicated that there is a strong relationship between runoff volume and vegetation cover, where a high where a high runoff volume was observed as a result of vegetation removal and urbanization. runoff volume was observed as a result of vegetation removal and urbanization.

Figure 8. Land use/land cover (LULC) map of the study area. Figure 8. Land use/land cover (LULC) map of the study area. 3.3. The Soil Conservation Service-Curve Number Map 3.3.The The SCS-CN Soil Con valuesservation for Service each cell-Curve were Number derived M byap the ArcGIS software on the basis of the AMC-II, HSG map,The andSCS LULC-CN values map for (Figure each9 cell). As were shown derived in Figure by the9 ,ArcGIS the values software of CN on IItherange basis from of the 74 AMC in - agriculturalII, HSG map, areas and to 93LULC in urban map and(Figure bare 9). rock As areas.shown Additionally, in Figure 9, CNthe Ivaluesand CN ofIII CNvaluesII range associated from 74 in withagricultural AMC-I and areas AMC-III, to 93 in respectively, urban and werebare determinedrock areas. Additionally, using Equations CN (3)I and and CN (4)III (Figurevalues associated10A,B). Figurewith 10 AMCA,B- showI and thatAMC the-III, lowest respectively, CNs values were were determined found to usin be 54g Equations and 87, whereas (3) and the(4) highest(Figure CNs10A,B). valuesFigure were 10A,B 85 and show 97 forthat CN theI andlowest CN CNsIII, respectively. values were These found CN to Ibe, CN 54II and, and 87, CN wherIII valueseas the emphasize highest CNs thatvalues the Wadi-Uranah were 85 and basin97 for has CN manyI and CN placesIII, respectively. with high impermeable These CNI, CN zones,II, and distinguished CNIII values by emphasize rocks’ generalthat the physical Wadi characteristics.-Uranah basin has In addition, many places the region with high has no impermeable or little vegetation zones, coverage,distinguished which by also rocks' contributesgeneral physical to higher characteristics. levels of runo Inﬀ addirates.tion, The the CN regionII values has no were or furtherlittle vegetation corrected coverage, using slope which dataalso to contributes derive slope-corrected to higher levels or slope-adjusted of runoff rates. CN TheII (Figure CNII values 11). Figure were further11 shows corrected that the using highest slope slope-adjusteddata to derive CN slopeII (95)-corrected was associated or slope with-adjusted a steep CN slopeII (Figure of the study11). Figure area ( >1180%), shows while that the the lowest highest slope-adjustedslope-adjusted CN CNII (74)II (95) was was found associated in the areaswith thata steep have slope a slight of the slope study (< 20%).area (>80%), while the lowest slope-adjusted CNII (74) was found in the areas that have a slight slope (<20%).

Water 2020Water, 122020, 1924, 12, x FOR PEER REVIEW 12 of 1711 of 16 Water 2020, 12, x FOR PEER REVIEW 12 of 17 Water 2020, 12, x FOR PEER REVIEW 12 of 17

FigureFigure 9. 9. Curve number (CNII) associated with AMC-II for each cell of the study area. FigureFigureCurve 9. 9. Curve Curve number number number (CN (CN (CNII)IIII associated)) associatedassociated withwith AMC AMC-II--IIII forfor for eacheach each cell cell cellof of the the of study thestudy study area. area. area.

Figure 10. (A) Curve number (CNI) associated with AMC-I for each cell of the study area, (B) curve FigureFigureFigure 10. ( 10.A 10.) Curve( A(A) )Curve Curve number number number (CN (CN (CNI)I) associatedI )associated associated withwith AMCAMC AMC-I--II forfor for eacheach each cell cell of the of the studystudy study area,area, area, ((BB)) curvecurve (B) curve number (CNIII) associated with AMC-III for each cell of the study area. numbernumbernumber (CN (CNIII (CN) associatedIIIIII) )associated associated with with with AMC-III AMC AMC-III-III forfor for eacheach each cell cellcell ofof of thethe the studystudy study area. area.

Figure 11. Slope-adjusted CNII map for each cell of the study area. Figure 11. Slope-adjusted CNII map for each cell of the study area. 3.4. Potential Maximum Retention Map Figure 11. Slope-adjusted CNII map for each cell of the study area. Figure 11. Slope-adjusted CNII map for each cell of the study area.

Once the slope-adjusted CNII map is generated, the potential maximum retention (S) is calculated in ArcGIS software using Equation (2) (Figure 12). As shown in Figure 12, the values of S range from 15 to 85 mm. The lowest S values within the basin are located in built-up areas, where the retention capacity is low. Similarly, bare areas located near municipalities had a poorer retention capacity as long Water 2020, 12, x FOR PEER REVIEW 13 of 17

3.4. Potential Maximum Retention Map

Once the slope-adjusted CNII map is generated, the potential maximum retention (S) is Water 2020calculated, 12, 1924 in ArcGIS software using Equation (2) (Figure 12). As shown in Figure 12, the values of S12 of 16 range from 15 to 85 mm. The lowest S values within the basin are located in built-up areas, where the retention capacity is low. Similarly, bare areas located near municipalities had a poorer retention as theycapacity are improperly as long as used.they are On improperly the other used. hand, On agricultural the other hand, areas agricultural have the areas highest have Sthe values highest as they have aS high values retention as they have capacity. a high Noteworthy retention capacity. is that N mostoteworthy of the is studythat most area of isthe covered study area by is the covered S values of 15–50 mm.by the S values of 15–50 mm.

Figure 12. Potential maximum retention (S) map of the study area. Figure 12. Potential maximum retention (S) map of the study area. 3.5. The Soil Conservation Service Runoff Map 3.5. The Soil Conservation Service Runoff Map The S and maximum daily rainfall maps were used as inputs in the GIS-based SCS-CN model to The S and maximum daily rainfall maps were used as inputs in the GIS-based SCS-CN model to calculatecalculate the daily the daily runo runoff.ff. The The resulting resulting raster raster ofof runorunoffff depthdepth is is displayed displayed in Figure in Figure 13. A 13 variation. A variation that rangesthat ranges from 15from mm 15 intomm ainto maximum a maximum of 47of mm47 mm was was observed observed due due to to didifferencesfferences in topography topography and climateand in theclimate study in the area. study The area regions. The regions most exposed most exposed to surface to surface runo runoffff are built-upare built- areas,up areas, where where the CN frequentlythe CN exceeds frequently the value exceeds of the 90. value Furthermore, of 90. Furthermore, the mountainous the mountainous area has aarea high has runo a highff depth runoff due to its landdepth cover due condition, to its land steepcover condition slope, and, ste soilep slope type, and that soil has type a high that has share a high of fineshare particles. of fine particles. As a result, the outflowAs a result, can occurthe outflow more can easily occur in more areas easily with in these areas characteristics.with these characteristics. On the otherOn the hand, other hand, the lowest valuest ofhe the lowest runo valuesff occur of the in therunoff southwestern occur in the part southwestern of the study part area of the due study to the area high due potential to the high of water potential of water interception and water retention caused by vegetation coverage and loam soil interceptionWater 2020, and12, x FOR water PEER retention REVIEW caused by vegetation coverage and loam soil texture type, respectively.14 of 17 texture type, respectively.

Figure 13. Potential runoﬀ map of the study area. Figure 13. Potential runoff map of the study area.

3.6. The Soil Conservation Service Storm Hydrograph Design storm hydrographs for selected recurrence intervals (10, 50, and 100 years) were developed for the Wadi-Uranah basin (Figure 14). Figure 14 shows that the design frequency of the 10-years return period, a 24-h storm hydrograph, has a peak flow and peak time of 828 m3/s and 17.5 h, respectively. For 50 and 100 years, 24-h storm hydrographs, the peak flow and peak time were (1353 m3/s and 17.5 h) and (1603 m3/s and 17.5 h), respectively. These results are in agreement with Salami et al. [45] who concluded that the SCS method can be used for the development of peak storm hydrographs of different river return periods.

1800

1600

1400 )

/s 1200 10-yr, 24-hr Runoff hydrograph 3

m 50-yr, 24-hr Runoff hydrograph ( 1000 100-yr, 24-hr Runoff hydrograph 800

600

Discharge 400

200

0 0 20 40 60 80 100 Time (h)

Figure 14. The Soil Conservation Service storm hydrograph of different return periods for the study area.

3.7. Rainfall-Runoff Correlation Analysis The scatter-plot analysis shown in Figure 15 indicates a strong linear relationship between the maximum daily rainfall and SCS-CN runoff with a correlation coefficient of 0.98. This coefficient may provide valuable information on the extent of the basin response to runoff generation. Our study

Water 2020, 12, x FOR PEER REVIEW 14 of 17

Water 2020, 12, 1924 13 of 16

Figure 13. Potential runoff map of the study area. 3.6. The Soil Conservation Service Storm Hydrograph 3.6. The Soil Conservation Service Storm Hydrograph Design storm hydrographs for selected recurrence intervals (10, 50, and 100 years) were developed for the Wadi-UranahDesign storm basin hydrographs (Figure 14 ). for Figure selected 14 shows recurrence that interval the designs (10, frequency 50, and 100 of the years) 10-years were return developed for the Wadi-Uranah basin (Figure 14). Figure 14 shows that the design frequency of the period, a 24-h storm hydrograph, has a peak flow and peak time of 828 m3/s and 17.5 h, respectively. 10-years return period, a 24-h storm hydrograph, has a peak flow and peak time of 828 m3/s and 17.5 3 For 50h, and respectivel 100 years,y. For 24-h 50 and storm 100 years, hydrographs, 24-h storm the hydrographs, peak flow the and peak peak flow time and were peak (1353time were m / s and 3 17.5 h)(1353 and m (16033/s and m 17.5/s and h) and 17.5 (1603 h), respectively. m3/s and 17.5 Theseh), respectively. results are These in agreementresults are in with agreement Salami with et al. [45] who concludedSalami et al. that [45] thewho SCS concluded method that can the be SCS used method for thecan developmentbe used for the development of peak storm of peak hydrographs storm of differenthydrographs river return of different periods. river return periods.

1800

1600

1400 )

/s 1200 10-yr, 24-hr Runoff hydrograph 3

m 50-yr, 24-hr Runoff hydrograph ( 1000 100-yr, 24-hr Runoff hydrograph 800

600

Discharge 400

200

0 0 20 40 60 80 100 Time (h)

FigureFigure 14. The 14. Soil The ConservationSoil Conservation Service Service storm storm hydrograph hydrograph of of di differentfferent return return periodsperiods for the the study study area. area. 3.7. Rainfall-Runoff Correlation Analysis The3.7. scatter-plotRainfall-Runoff analysis Correlation shown Analysis in Figure 15 indicates a strong linear relationship between the maximumThe daily scatter rainfall-plot andanalysis SCS-CN shown runo in Figureff with 15 a indicates correlation a strong coeffi linearcient relationship of 0.98. This between coefficient the may providemaximumWater valuable 2020, daily12, x information FOR rainfall PEER REVIEWand onSCS the-CN extent runoff with of the a correlation basin response coefficient to runoof 0.98.ff Thisgeneration. coefficient15 of Our may 17 study resultsprovide are in linevaluable with information the findings on of the Tirkey extent et of al. the [10 basin] who response found ato goodrunoff coe generation.fficient of Our determination study results are in line with the findings of Tirkey et al. [10] who found a good coefficient of determination (0.891) for a large study area using SCS-CN model. The slope of the trend line in the graph reflects the (0.891) for a large study area using SCS-CN model. The slope of the trend line in the graph reflects runoff coefficient, which is around 0.83. Such findings agreed with Peng and You [46] who came to the the runoff coefficient, which is around 0.83. Such findings agreed with Peng and You [46] who came conclusionto the that conclusion the SCS-CN that the model SCS- hasCN modela better has simulation a better simulation effect on effect study on areas study with areas a with coeffi a cient of runoff greatercoefficient than of runoff 0.5 than greater those than with 0.5 than a coe thosefficient with of a coefficient runoff less of thanrunoff 0.5. less than 0.5.

34 Equation y = a + b*x Intercept -17.86 ± 0.041 Slope 0.833 ± 7.23E-4 32 R-Square 0.988

28 Runoff (mm)

24 50 52 54 56 58 60 62 64 Rainfall (mm)

Figure 15. The rainfall and SCS-CN runoﬀ relationship for the study area. Figure 15. The rainfall and SCS-CN runoff relationship for the study area.

4. Conclusions The limited availability of measured surface runoff restricts various activities of water resources development and management. To overcome this limitation, the GIS, RS, and SCS-CN methods were combined to estimate the surface runoff in Wadi-Uranah basin, Saudi Arabia. The DEM, HSG, LULC, and daily precipitation records were used as inputs in the GIS-based SCS-CN model. Based on soil classification results, the study area was classified into two HSGs (B and C). The dominant HSG was group C, representing about 98.8% of the total area. Curve number values for the normal conditions (CNII) ranged from 74 in the agricultural areas to 93 in the urban and rock areas. The CNII values were further corrected using slope data to derive slope-adjusted CNII (CNIIα). The highest CNIIα (95) was associated with a steep slope (>80%) of the study area, whilst the lowest CNIIα (74) was found in the areas that have a slight slope (<20%). Furthermore, the study showed that the behavior of the runoff varies spatially depending on the LULC and the permeability of the soil. The peak runoff hydrograph flows obtained from SCS based dimensionless unit hydrograph for 10-, 50-, and 100-year return periods were 828, 1353, and 1603 m3/s, respectively. Therefore, SCS-CN method integrated with RS and GIS deserves further attention to study watershed characteristics in other basins in Saudi Arabia.

Funding: This research received no external funding.

Acknowledgments: This project was supported by the Vice Deanship of Research Chairs at King Saud University.

Conflicts of Interest: The authors declare no conflict of interest.

Water 2020, 12, 1924 14 of 16

4. Conclusions The limited availability of measured surface runoff restricts various activities of water resources development and management. To overcome this limitation, the GIS, RS, and SCS-CN methods were combined to estimate the surface runoff in Wadi-Uranah basin, Saudi Arabia. The DEM, HSG, LULC, and daily precipitation records were used as inputs in the GIS-based SCS-CN model. Based on soil classification results, the study area was classified into two HSGs (B and C). The dominant HSG was group C, representing about 98.8% of the total area. Curve number values for the normal conditions (CNII) ranged from 74 in the agricultural areas to 93 in the urban and rock areas. The CNII values were further corrected using slope data to derive slope-adjusted CNII (CNIIα). The highest CNIIα (95) was associated with a steep slope (>80%) of the study area, whilst the lowest CNIIα (74) was found in the areas that have a slight slope (<20%). Furthermore, the study showed that the behavior of the runoff varies spatially depending on the LULC and the permeability of the soil. The peak runoff hydrograph flows obtained from SCS based dimensionless unit hydrograph for 10-, 50-, and 100-year return periods were 828, 1353, and 1603 m3/s, respectively. Therefore, SCS-CN method integrated with RS and GIS deserves further attention to study watershed characteristics in other basins in Saudi Arabia.

Author Contributions: Conceptualization, H.A.-G. and A.A.; data curation, A.A. and A.D.; formal analysis, A.D.; hydrology and basins analysis, A.D.; writing—original draft preparation, A.D.; writing—reviewing and editing, H.A.-G., A.D. and A.A. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: This project was supported by the Vice Deanship of Research Chairs at King Saud University. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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